Propagation Prediction Research Articles

A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD–SSO–LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO–LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD–SSO–LSTM model exhibits heightened predictive accuracy.

A prediction model for rumor user propagation behavior based on sparse representation and transfer learning

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed.

Three-Dimensional Ray-Tracing-Based Propagation Prediction Model for Macrocellular Environment at Sub-6 GHz Frequencies

This paper presents a 3D ray-tracing model using geometrical optics and the uniform theory of diffraction in radio channel characterizations of macrocellular environments. On the basis of the environmental information obtained from a digitized map, the model is effectively applied. A technique considering multiple reflections and diffractions through the ray path classification is utilized in this model. Ray paths belonging to each ray category are determined using different methods. The proposed model is justified (the prediction accuracy of the model is better than 6.5 dB) with measurement data for the two scenarios and can provide reliable theory as a basis for radio wave propagation prediction and network planning in urban macrocellular environments.

Fatigue life estimation of nickel-based single crystal superalloy with different inclined film cooling holes: Initial damage quantification and coupling of damage-fracture mechanics models

Quantitative assessment of the initial damage state of a structure and fatigue life prediction on this basis, especially when it is sensitive to manufacturing quality, is crucial for both engineering application and material science. Traditional solutions based on fracture or damage mechanics either require precise constitutive relationships and cumbersome physical mechanism models or simply ignore the initial damage state. This study developed a novel equivalent initial flaw size (EIFS) quantitative evaluation method for film cooling holes in Nickel-based single crystal superalloy turbine blades to address the strong correlation between the initial damage in and fatigue life of the material. The temperature and stress field differences at the edges of holes with different inclination angles were simulated using a solid-liquid-gas three-phase level-set model and considered to represent the same initial damage. Next, the coupled damage–fracture mechanics model was used to achieve equivalent crack insertion, crack propagation, and fatigue life prediction based on the EIFS. The results indicate that this method can provide a highly robust EIFS distribution interval and that the crack geometry correction factor and EIFS distribution range are weakly correlated with the loading conditions (with an error within 2 %). The EIFS-based fatigue life predictions demonstrated a notable degree of accuracy, with the majority of the predicted and experimental fatigue lives falling within a three-fold dispersion band and all predictions remaining confined within a five-fold dispersion band of the actual lifetime. These results represent a substantial advancement in accuracy compared to traditional damage mechanics predictions. Therefore, this study provides a powerful approach for evaluating the fatigue life of turbine blades considering the initial damage state and can be widely applied to guide drilling process optimization and blade fatigue analysis.

The effect of fibre orientation on fatigue crack propagation in CFRP: Finite fracture mechanics modelling for open-hole configuration

The demand to capture translaminar crack growth under fatigue loading scenarios led this work contribution to carry out the Finite Fracture Mechanics (FFM) method in fatigue damage growth and the application of the Paris model to generate the translaminar damage propagation prediction. The purpose of this study is to analyse the effect of fibre orientation on translaminar crack propagation rate using the FFM model, which includes cycle damage increment estimation and fractographic analysis. The results confirm the feasibility of FFM in predicting crack growth and estimating life under cyclic loading. However, C-scan analysis and the revised crack propagation direction are critical in determining the realistic crack length, considering adhesive failure along the fibre direction. Additionally, this work contribution is also related to the application of the Paris model (based on dL/dN vs ΔK) to generate the translaminar damage propagation prediction model. The most dominant damage mechanism was the splitting pattern, which changed the aspect of failure for each laminate architecture as a function of fibre orientation. The laminate with multidirectional fibre orientation exhibited higher resistance to translaminar crack propagation due to the growth of splitting and delamination in multiple directions. The fibre orientation changed the propagation path, which influenced the fracture toughness and crack propagation rate behaviour.

Insight into the shear failure mechanism of Wound FRP reinforced concrete beams through numerical simulations considering interface damage and material anisotropy

Utilizing the inherent flexibility of high-strength fibres, Fibre-Reinforced Polymers (FRP) can be engineered as optimized reinforcing cages for concrete structures through the application of advanced digital fabrication techniques. However, existing codified design methods and numerical investigations found in the literature fall short in providing an accurate characterization of the actual performance of FRP reinforcement in concrete structures. This work presents a novel Finite Element (FE) modelling approach for concrete beams reinforced with FRP in non-traditional arrangements, accounting for reinforcement-concrete interface behaviour and FRP material anisotropy. Validation against experimental tests of flexible Wound-FRP (W-FRP) reinforced beams demonstrates accurate predictions of load-displacement curves, crack propagation, and load-strain curves. The study delves into shear failure mechanisms, emphasizing FRP reinforcement behaviour and damage evolution at reinforcement-concrete interfaces. Further parametric analysis explores the impact of W-FRP inclination angle and spacing, revealing their significant influence on beam stiffness and ultimate capacity. Specifically, the horizontal tensile force component in inclined links and increased modulus of links with reduced spacing enhance stiffness. Altered crack propagation due to W-FRP patterns, coupled with resulting damage development at concrete-stirrup interfaces and stress evolution in W-FRP stirrups, are key contributors to varied ultimate capacity. Moreover, higher performance of W-FRP stirrups with smaller spacing and reduced cross-section area further bolsters ultimate capacity. This research advances the modelling of FRP-reinforced concrete, offering potential solutions for optimizing FRP material utilization in concrete structures.

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

An adaptive finite element method for coupled fretting wear and fatigue crack propagation simulation

Fretting wear and fatigue crack propagation often occur at the same time in the service of mechanically connected structures. In order to analyze the effect of fretting wear on the process of fatigue crack propagation, a finite element simulation method for fatigue crack propagation considering fretting wear is proposed. In this method, the Archard model is used to predict the wear process, and the conventional Paris equation is modified based on the wear rate. In order to improve the accuracy of crack propagation prediction, a crack-tip super singular element (CSSE) is used to calculate the singular stress field and fracture control parameters of the crack tip. An adaptive meshing technique that automatically adjusts the mesh density according to the simulation process is designed to ensure the computational efficiency of the algorithm and the accuracy of the numerical results. The smoothing technique used in wear simulation can alleviate the influence of contact stress fluctuation on wear profile prediction. The usage of the new simulation method is introduced through benchmark examples. It is proved that the numerical results of crack propagation obtained by the adaptive finite element method (FEM) have high precision, good convergence and high computational efficiency. It is found that wear reduces the crack growth rate compared with the simulation results without considering wear. This method has a promising application in the coupling analysis of fretting wear and fatigue crack propagation of mechanically connected structures.

Raster approach to modelling the failure of arbitrarily inclined interfaces with structured meshes

This paper presents an approach to evaluate the failure of arbitrarily inclined interfaces using FE models with structured spatial discretization, providing accurate prediction of crack propagation along paths known a priori that are not constrained to the element boundaries. The combination of algorithms for the generation of structured discretization of representative polycrystalline microstructures with novel cohesive element formulations allow modelling the failure of complex topologies along rasterised boundaries, with noticeably higher computational efficiency and comparable accuracy. Two formulations of raster cohesive elements are presented, adopting either elastic-brittle or Tvergaard–Hutchinson traction separation laws. The formulations proposed are first validated comparing the failure of the interface within bi-crystal structures discretised using hexahedral elements either within a structured mesh (i.e. with rasterised boundaries) or an unstructured mesh (i.e. with planar boundary). Subsequently, the effectiveness of the formulations is demonstrated comparing the inter-granular crack propagation within complex polycrystalline microstructures. The behaviour of the novel cohesive element formulation in structured meshes consisting of regular hexahedral elements is in excellent agreement with the deformation and failure of classic cohesive element formulations placed along the planar boundaries of unstructured meshes consisting of tetrahedral elements. The higher computational cost of the raster cohesive elements is more than compensated by the increase in computational efficiency of structured meshes when compared to unstructured meshes, leading to a reduction of the simulation time of up to over 200 times for the simulations presented in the paper, thus allowing the simulation of large domains.

A Bayesian network‐based susceptibility assessment model for oil and gas pipelines suffering under‐deposit corrosion

AbstractOil and gas pipelines are exposed to harsh operating conditions that facilitate their susceptibility to complex corrosion mechanisms. This affects their integrity and results in failure with associated consequences. Capturing these complex corrosion phenomena requires a robust approach. This study proposes the application of a dynamic probabilistic model to capture the key influential factors that contribute to the complex under‐deposit corrosion (UDC) mechanism in oil and gas pipelines. The Bayesian network model assesses the pipeline's susceptibility (degradation rate) to the UDC, capturing parametric dependencies. The predicted corrosion rates are input data for the corrosion propagation prediction. Three semi‐empirical corrosion propagation models are used for a comparative assessment to establish the degree of susceptibility given the prevalent influential factors and model parameters. The proposed approach is tested on an offshore pipeline, and the degree of impact of the key influential parameters is predicted. The result shows a percentage increase in the degradation rate by 18.7%, 33.2%, 35.8%, and 63.4%, respectively, for the various interaction scenarios. The present approach offers an adaptive and robust technique that would provide an early warning guide on the rate of pipeline degradation to aid integrity management for offshore assets suffering from deposit corrosion.

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

Blockage Effects of Road Bridge on mmWave Channels for Intelligent Autonomous Vehicles

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

FAST-CA: Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention for airport network delay propagation prediction

The issue of delay propagation prediction in airport networks has garnered increasing global attention, particularly due to its profound impact on operational efficiency and passenger satisfaction in modern air transportation systems. Despite research advancements in this domain, existing methodologies often fall short of comprehensively addressing the challenges associated with predicting delay propagation in airport networks, especially in terms of handling complex spatial–temporal dependencies and sequence couplings. In response to the complex challenge of predicting delay propagation in airport networks, we introduce the Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention (FAST-CA) framework. FAST-CA is an innovative model that integrates dynamic and adaptive graph learning, coupled attention mechanisms, periodicity feature extraction, and multifaceted information fusion modules. This holistic approach enables a thorough analysis of the interplay between flight departure and arrival delays and the spatial–temporal correlations within airport networks. Rigorously evaluated on two extensive real-world datasets, our model consistently outperforms current state-of-the-art baseline models, showcasing superior predictive performance and the effective learning capabilities of its intricately designed modules. Our research highlights the criticality of analyzing spatial–temporal relationships and the dynamics of flight coupling, offering significant theoretical and practical contributions to the advancement and management of air transportation systems.

Study of Noise Effect of Slag Storage Technology on Surrounding Environment

The metallurgical sector is one of the important sectors of the Slovak economy. Its integral part is the storage of metallurgical waste, which is accompanied by noise that bothers the inhabitants of the surrounding urban areas. This paper focuses on the analysis of the problem of noise propagation into protected areas located in the vicinity of the metallurgical plant. The paper describes a number of measurements that have been carried out at the slag landfill. Based on these measurements, simulations were performed using a mathematical model, and predictions of noise propagation in the exterior were made. Subsequently, noise reduction measures were proposed. The results obtained by the authors form a methodological basis for addressing such situations, since, during the solution, it was often necessary to deal with non-standard situations that were specific to the area of the technology addressed. This solution was then applied in real practice.

Information Propagation Prediction Based on Spatial–Temporal Attention and Heterogeneous Graph Convolutional Networks

With the development of deep learning and other technologies, the research of information propagation prediction has also achieved important research achievements. However, the existing information diffusion studies either focus on the attention relationships of users or they predict the information according to the diffusion relationships of users, which makes the prediction results have certain limitations. Therefore, a prediction model has been proposed spatial–temporal attention heterogeneous graph convolutional networks (STAHGCNs). First, we use GCN to learn user influence relationships and user behavior relationships, and we propose a user representation fusion mechanism to learn the user characteristics. Second, to account for the dynamics of user behavior, a temporal attention mechanism strategy is used to encode time into the heterogeneous graph to obtain a more expressive user representation. Finally, the obtained user representation is input into the multihead attention mechanism for information propagation prediction. Experimental results performed on the Twitter, Douban, Digg, and Memetracker datasets have shown that the proposed STAHGCN model increased by 8.80% and 6.74% at hits@N and map@N, respectively, which are significantly better than the original latest DyHGCN model. The proposed STAHGCN model effectively integrates spatial factors, such as time factor, user influence, and behavior, which greatly improves the accuracy of information propagation prediction and has great significance for rumor monitoring and malicious account detection.

Misurata Rural Area Path Loss Propagation Prediction and Optimization Using Machine Learning Techniques

Path loss prediction is of great importance in studies of designing, developing, and improving the performance of wireless networks and mobile communication systems of the 5G which requires of accuracy in performance and reduction in losses. Therefore, new models should be proposed to predict path loss propagation with high accuracy and less complexity. In this paper, a path loss model was designed and implemented using artificial intelligence and machine learning techniques, which training and testing on a real database created based on maximum measured distance and received power from the base station in a GSM system operating in 900 MHz band, in a rural environment, Misurata, Libya. Traditional methods like the "simple path loss model" performed poorly, with an average error of 5.87. In contrast, modern models based on artificial intelligence (AI) like GRNN, Fitnet, and Feedforward Net achieved significantly lower errors, ranging from 0.26 to 1.35. Furthermore, the AI models showed a strong correlation between the predicted and actual signal strengths (correlation coefficient R reaching 0.9962 for Fitnet and 0.99399 for Feedforward Net). This indicates highly specific and reliable predictions. Keywords: wireless communications, propagation loss prediction, ANN, machine learning, GRNN, urban area

Estimation and prediction of freeway traffic congestion propagation using tagged vehicle positioning data

Effective estimation and prediction of freeway congestion propagation is the basis for formulating a traffic management strategy. A new estimation and prediction model for congestion propagation based on coordinated vehicle positioning data collected from fixed detectors is proposed. A shockwave-based method is established to model the propagation trajectory of congestion in time and space using data filtered by the Savitzy-Golay. Bayesian ridge regression is applied to determine the probability range of the propagation path of the congestion. The proposed method was tested with Wi-Fi positioning data from a section of the Beijing-Kunming Freeway in China. The results of our method were compared with filed traffic conditions estimated by loop data. The results show a difference of approximately 200s between the start time of the estimated congestion and the observed time on the selected road section. The predicted congestion start time differs from the observed results by around 100s.

Medium-range forecasting of oceanic eddy trajectory

ABSTRACT The capability of continuous eddy tracking has been significantly advanced over the past decades, thanks to the availability of sea surface height measurements routinely collected by satellite altimeters. However, due to the complexity of eddy kinematic mechanisms and underlying oceanic processes, a medium-range forecast of individual eddy trajectories is a challenging task. This study develops an algorithm to effectively achieve a precise prediction of eddy propagation with a ten-day lead time by building a physics-embedded temporal evolution model. The theoretical eddy phase speed, current field, and eddy properties are incorporated as physical constraints. Compared to only considering eddy past/climatological velocities, the forecast accuracy of embedded physics constraints is improved by 21%. Forecasting of 2.3 million altimeter-derived eddy tracks globally shows that trajectory predictions are valid for eddies moving towards any direction, and the forecast error achieves 9.9 km on a ten-day average. After systematically evaluating the global forecast error, we conclude that the forecasting accuracy is geographically correlated and affected by external forcings (topography and wind) and eddy properties (geostrophy and life stage). Achieving accurate eddy trajectory prediction is expected to provide critical guidance for in-situ eddy observations, and bring substantial improvements for understanding oceanic substance transportations and biogeochemical processes.

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as-to our knowledge-no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

Deep learning based prediction of urban air mobility noise propagation in urban environment.

A deep learning based method is proposed to predict the urban air mobility (UAM) noise propagation in the urban environment. This method aims to efficiently estimate the noise impact of UAM flights on the complex urban area. The noise hemisphere was created via the comprehensive multirotor noise assessment framework to determine the noise level of UAM. The noise propagation to a randomly generated three-dimensional (3D) urban area was then calculated using the ray tracing method, including atmospheric attenuation and multiple reflections. 45 000 two-dimensional noise maps were used to train and evaluate the modified convolutional neural network. The results demonstrated high accuracy, with a root mean square error of only 2.56 dB compared to the ray tracing method, while reducing computation time by more than 1800 times. This model was applied to analyze the noise impact of various UAM flight conditions and landing scenarios at a vertiport. This deep learning approach is a fast method with adequate accuracy for predicting UAM noise impact in 3D urban environments. Also, it can inform the development of noise based strategies for UAM operations.